TwinStrand Duplex Sequencing Target Gene Panel

Description

This document describes the steps, code, and files used to generate the ~50kb TwinStrand sequencing panel to study the genetics of male infertility. Specifically, we are targeting DNA damage repair genes, known cancer genes, AZF genes, and genes involved in clonal spermatogenesis.

Pre-Processing of Necessary Files

Target Gene List

This is the script used to generate a targeted sequencing panel for the spermseq male infertility project. )

Step 1: Copy the gene list from the spreadsheet and save as a 1-column text file (target_genes.txt) Step 2: Run below script to save into kingspeak UNIX environment

## Change working directory 
cd ~/git/spermseq/twinstrand_target_gene_set
## Sort and unique 
cat target_genes.txt | sort | uniq > target_genes_v1.txt
## Copy to kingspeak directory
scp target_genes_v1.txt u1240855@kingspeak.chpc.utah.edu:~/spermseq/twinstrand_targets

GRCh38 GTF File

Use the build 38 GTF file to get other identifying information for each gene_name of interest. Look only at genes/regions that are coding sequences (hence grep -w “CDS” in the below command…)

## Define directory
cd ~/spermseq/twinstrand_target

## Download GTF file 
wget ftp://ftp.ensembl.org/pub/release-102/gtf/homo_sapiens/Homo_sapiens.GRCh38.102.gtf.gz | zless | grep -w "CDS" > Homo_sapiens.GRCh38.102.CDS.gtf
scp u1240855@kingspeak.chpc.utah.edu:~/spermseq/twinstrand_targets/Homo_sapiens.GRCh38.102.CDS.gtf ~/git/spermseq/twinstrand_target_gene_set/data

## Get the gene_ids of the target genes of interest
cat target_genes_v1.txt | awk '{print "cat Homo_sapiens.GRCh38.102.CDS.gtf | grep \"gene_name \\\""$1"\\\"\""}' | bash | cut -f 9 | awk 'BEGIN{FS = "\"; "} ; {print $0}' | sed 's/\"; /|/g' | sed 's/;//g' | sed 's/ \"/=/g' > target_gene_list_gtf_identifiers.txt

## Copy target_genes_gid_tid_pid.txt file to local 
scp u1240855@kingspeak.chpc.utah.edu:~/spermseq/twinstrand_targets/target_gene_list_gtf_identifiers.txt ~/git/spermseq/twinstrand_target_gene_set

Use R script to get gene identifier information from the GTF file

Use R to obtain key GTF fields for each gene: gene_name, gene_id, transcript_id, protein_id, and exon_number.

## Load in packages
library(data.table)
library(dplyr)
## Read in the file 
df <- read.table("~/git/spermseq/twinstrand_target_gene_set/target_gene_list_gtf_identifiers.txt")

## Go through each row and get the gene_id, gene_name, transcript_id, protein_id, exon_number, and exon_id
x <- df[1,]
ids <- rbindlist(apply(df, 1, function(x) {
  fields <- (strsplit(x, split = "|", fixed = TRUE))[[1]]
  fields <- fields[grep(paste(c("gene_name", "gene_id", "transcript_id", "protein_id", "exon_number"), collapse = "|"), fields)] %>% 
    strsplit(., split = "=", fixed = TRUE) %>% unlist()
  
  return(data.table("gene_name"=fields[8],
                    "gene_id"=fields[2],
                    "transcript_id"=fields[4],
                    "protein_id"=fields[10],
                    "exon_number"=fields[6]))
}))
write.table(x = ids, file = "~/git/spermseq/twinstrand_target_gene_set/target_gene_list_ids.txt", quote = FALSE, sep = "\t", row.names = FALSE)

GTEx gene-level expression data

Obtain GTEx TPM data using gene_ids belonging to the genes of interest.

## Copy the gene list ids file to kingspeak 
scp ~/git/spermseq/twinstrand_target_gene_set/target_gene_list_ids.txt u1240855@kingspeak.chpc.utah.edu:~/spermseq/twinstrand_targets

## Download master sample list to identify testis samples
cd ~/spermseq/data/GTEx/samples
wget https://storage.googleapis.com/gtex_analysis_v8/annotations/GTEx_Analysis_v8_Annotations_SampleAttributesDS.txt
grep -w -i "testis" GTEx_Analysis_v8_Annotations_SampleAttributesDS.txt | awk '{print $1}' > GTEx_Analysis_v8_Annotations_SampleAttributesDS_TestisOnlySamples_Data.txt

## Download master transcript TPM data
cd ~/spermseq/data/GTEx/transcripts
wget https://storage.googleapis.com/gtex_analysis_v8/rna_seq_data/GTEx_Analysis_2017-06-05_v8_RSEMv1.3.0_transcript_tpm.gct.gz 
gunzip GTEx_Analysis_2017-06-05_v8_RSEMv1.3.0_transcript_tpm.gct.gz

## Obtain GTEx transcript TPM data from genes of interest --> see "Get TPM data from GTEx testis samples"
cd ~/spermseq/data/GTEx/transcripts
head -n 1 GTEx_Analysis_2017-06-05_v8_RSEMv1.3.0_transcript_tpm.gct > header.temp ## Get the header which contains sample ids 
## Run this if the file already exists: rm ~/spermseq/data/GTEx/transcripts/GTEx_Analysis_2017-06-05_v8_RSEMv1.3.0_TESTIS_transcript_tpm.gct
cat ~/spermseq/twinstrand_targets/target_gene_list_ids.txt | awk -v OFS='\t' '{print $1, $2}' | sort | uniq | grep -v "gene_name" | cut -f 2 | awk '{print "cat $HOME/spermseq/data/GTEx/transcripts/GTEx_Analysis_2017-06-05_v8_RSEMv1.3.0_transcript_tpm.gct | grep "$1" "}' | bash >> ~/spermseq/data/GTEx/transcripts/testis.data.temp
cat header.temp testis.data.temp > GTEx_Analysis_2017-06-05_v8_RSEMv1.3.0_TESTIS_transcript_tpm.gct
## Remove files 
rm header.temp
rm testis.data.temp

## Copy final file to local directory
scp u1240855@kingspeak.chpc.utah.edu:~/spermseq/data/GTEx/transcripts/GTEx_Analysis_2017-06-05_v8_RSEMv1.3.0_TESTIS_transcript_tpm.gct ~/git/spermseq/twinstrand_target_gene_set/data

APPRIS isoform data

## Download appris principal isoform data
cd ~/spermseq/data/appris
wget http://apprisws.bioinfo.cnio.es/pub/current_release/datafiles/homo_sapiens/GRCh38/appris_data.principal.txt

## Copy to local directory
scp u1240855@kingspeak.chpc.utah.edu:~/spermseq/data/appris/appris_data.principal.txt ~/git/spermseq/twinstrand_target_gene_set/data

General analysis workflow

[insert image]

R scripts:

Set up R environment

knitr::opts_chunk$set(echo=TRUE)

######################################
##### Load in necessary packages #####
######################################
library(data.table)
library(dplyr)
library(ggplot2)
library(beeswarm)
library(ggbeeswarm)
library(reshape)
library(ggpubr)
library(biomaRt)
library(ensembldb)
library(EnsDb.Hsapiens.v86)

##################################
##### Specify file locations #####
##################################
appris_file <- "~/git/spermseq/twinstrand_target_gene_set/data/appris_data.principal.txt"
gtf_file <- "~/git/spermseq/twinstrand_target_gene_set/data/Homo_sapiens.GRCh38.102.CDS.gtf"
GTEx_file <- "~/git/spermseq/twinstrand_target_gene_set/data/GTEx_Analysis_2017-06-05_v8_RSEMv1.3.0_TESTIS_transcript_tpm.gct"
GTEx_samples <- "~/git/spermseq/twinstrand_target_gene_set/data/GTEx_testis_samples.txt"

#########################################
##### Specify the genes of interest #####
#########################################
genes <- c("WT1", "CDC14A", "DMRT1", "KLHL10", "M1AP", "MEI1", "STAG3", 
           "SYCP2", "SYCP3", "TEX11", "USP26", "PKD1", "AR", "AKT3", "APOA1", 
           "APC", "ATM", "BRAF", "BRCA1", "BRCA2", "CBL", "CDKN2A", "CTNNB1", "DICER1", 
           "DNMT3A", "ERCC1", "ESR1", "FANCM", "FGFR2", "FGFR3", "H3-3A", "H3-3B", "HRAS", 
           "KIT", "KRAS", "LIG4", "MAP2K1", "MAP2K2", "MLH1", "MLH3", "MSH5", "NR5A1", "NF1", 
           "PMS2", "PPM1D", "PRKACA", "PTCH1", "PTPN11", "RAG1", "RAF1", "RB1", "RET", "SMAD4", 
           "SOS1", "STAT3", "STK11", "TEX14", "TEX15", "TSHR", "VHL", "XPA", "XRCC1")
moderate_genes <- c("USP26", "TEX14", "SYCP2", "STAG3", "PKD1", "M1AP", "KLHL10", "DMRT1", "CDC14A")
moderate_gene_ids <- c("ENSG00000134588", "ENSG00000121101", "ENSG00000196074", "ENSG00000066923", "ENSG00000008710", "ENSG00000159374", "ENSG00000161594", "ENSG00000137090", "ENSG00000079335")
genes <- genes[-which(genes %in% moderate_genes)]
genes <- c(genes, "USP9Y", "DDX3Y", "PRY", "DAZ1", "DAZ2", "DAZ3", "DAZ4", "DAZL", "CDY1", "CDY1B", "RBM5")

Get TPM data from GTEx testis samples

if (file.exists("~/git/spermseq/script/final.Rdata") == FALSE) {
  
  source("~/git/spermseq/script/testis_GTEx_samples.R")
  testis_GTEx_tpm <- testis_GTEx_samples(GTEx_file = GTEx_file, GTEx_samples = GTEx_samples)
  head(testis_GTEx_tpm)
}

Read in the APPRIS isoform data

See APPRIS for column details… SANITY CHECK: This steps check that each gene of interest has an APPRIS isoform tag. If not, it will obtain the gene_id from biomaRt using the gene_name. No transcript_ids or CCDS_id will be obtained and the ‘status’ column will be flagged with a “minor” APPRIS designation.

if (file.exists("~/git/spermseq/script/final.Rdata") == FALSE) {
  source("~/git/spermseq/script/appris_manipulation.R")
  appris_df <- appris_manipulation(appris_file = appris_file, genes = genes) 
  head(appris_df)
}

Get the GTEx testis TPM data of each APPRIS transcript

if (file.exists("~/git/spermseq/script/final.Rdata") == FALSE) {
  
  source("~/git/spermseq/script/appris_manipulation.R")
  appris_df_tpm <- get_appris_tpm(appris_df = appris_df, testis_GTEx_tpm = testis_GTEx_tpm)
  appris_df_tpm <- data.table(do.call("rbind", appris_df_tpm)) %>% `colnames<-`(c("gene_name", "gene_id", "transcript_id", "CCDS", "status", "avg_GTEx_TPM"))
  
  appris_df_tpm
  
  ## See which appris isoforms did not have GTEx values 
  #genes[! genes %in% unique(appris_df_tpm$gene_name)]
}

Get GTEx testis TPM data for transcripts not in the APPRIS dataset

These are transcripts that are not identified as PRINCIPAL isoforms by APPRIS, but have avg TPM values across all testis samples > the minimum value of the APPRIS-defined PRINCIPAL isoforms.

NOTE: The GTEx-only transcript must have an avg TPM > 1 across all testis samples to be included in the dataset

  if (file.exists("~/git/spermseq/script/final.Rdata") == FALSE) {
    source("~/git/spermseq/script/gtex_transcript_tpm_manipulation.R")
    appris_gtex_transcript_tpm <- gtex_transcript_tpm_manipulation(appris_df_tpm = appris_df_tpm, testis_GTEx_tpm = testis_GTEx_tpm, moderate_gene_ids = moderate_gene_ids)
    
    # ## Run this command to filter out appris exons not reliably defined as principal exons
    # appris_gtex_transcript_tpm <- appris_gtex_transcript_tpm[!(status %in% c("PRINCIPAL:4", "PRINCIPAL:5", "ALTERNATIVE:1", "ALTERNATIVE:2"))]
    # file.remove("~/git/spermseq/script/gtf_df.Rdata")
    
    appris_gtex_transcript_tpm
  }

Use GTF file to get exon positions within each transcript (multiple transcripts belong to the same gene)

if (file.exists("~/git/spermseq/script/final.Rdata") == FALSE) {

  ## This is the longest step, see if the data already exists 
  # if (file.exists("~/git/spermseq/script/gtf_df.Rdata")) {
  #   load("~/git/spermseq/script/gtf_df.Rdata")
  # }
  
  if (!file.exists("~/git/spermseq/script/gtf_df.Rdata")) {
    ## Read in the gtf file 
    gtf <- fread(gtf_file, header=FALSE) %>% .[,c(1,4,5,9)] %>% `colnames<-` (c("chr", "start", "stop", "info")) 
    
    ## Get exon positions
    source("~/git/spermseq/script/gtf_manipulation.R")
    gtf_df <- gtf_manipulation(gtf = gtf, df = appris_gtex_transcript_tpm)
    head(gtf_df)
    save.image("~/git/spermseq/script/gtf_df.Rdata")
    
    ## Write output to text file 
    write.table(x = gtf_df, file = "~/git/spermseq/twinstrand_target_gene_set/output/appris_gtex_gtf_testis.txt",
                quote = FALSE, sep = "\t", row.names = FALSE)  
  }
}

Get the Pfam domain information for each exon

Schematic depicting how different exons across isoforms of a gene are merged.

if (file.exists("~/git/spermseq/script/final.Rdata") == FALSE) {
  ## Output the warning messages of the pfam domain exon analysis 
  if (file.exists("~/git/spermseq/script/get_pfam_log.txt")) {
    file.remove("~/git/spermseq/script/get_pfam_log.txt")
  }
  ## Capture output to a file
  sink_file <- file("~/git/spermseq/script/get_pfam_log.txt", open = "wt")
  sink(sink_file)
  sink(sink_file, type = "message")
  
  ## Run the function
  source("~/git/spermseq/script/get_pfam.R")
  pfam_df <- get_pfam(df = gtf_df)
  
  ## Output the data
  pfam_df
  
  ## Revert output back to the console
  sink(type = "message")
  sink()
}

Merge overlapping exons across transcripts of the same gene

Schematic depicting how different exons across isoforms of a gene are merged.

if (file.exists("~/git/spermseq/script/final.Rdata") == FALSE) {
  source("~/git/spermseq/script/merge_exons.R")
  merged_exons <- merge_exons(df = pfam_df)
  
  ## Get the total panel space (exonic)
  total <- sum(merged_exons$exon_size)
  
  ## Calculate proportion of panel space taken up by each gene 
  merged_exons$proportion_total <- merged_exons$total_nucleotides/total*100
  #head(merged_exons)
  
  ## Remove exons found below threshold of total proportion of transcripts for a given gene:
  source("~/git/spermseq/script/transcript_proportion_filter.R")
  merged_exons <- transcript_proportion_filter(merged_exons = merged_exons)
  
  ## Write the output file 
  write.table(x = merged_exons, file = paste0("~/git/spermseq/twinstrand_target_gene_set/output/merged_exons_.txt"), 
              quote = FALSE, sep = "\t", row.names = FALSE)
}

Output of master data

Summary

#summary(merged_exons$exon_size)

All exons

# ## Factor the data so that it plots the x-axis in an order
# merged_exons$x_axis <- paste0(merged_exons$gene_name, " (n = " , merged_exons$total_exons, " exons; avg = ", merged_exons$avg_exon_size, ")")
# merged_exons$x_axis <- factor(merged_exons$x_axis, levels = unique(merged_exons$x_axis[order(merged_exons$total_nucleotides, decreasing = TRUE)]))
# 
# ## Generate scatter plot --> Exons (y) per gene (x)
# p1 <- ggplot(data = merged_exons) + geom_point(aes(x = x_axis, y = exon_size, color = transcript_num)) + 
#   theme(axis.ticks.x = element_blank(), axis.text.x = element_text(angle = 75, hjust = 1)) + 
#   labs(x = "Gene", y = paste0("Exon Size"), color="# of Transcripts") + scale_y_log10() + 
#   scale_color_gradient(low = "grey", high = "red")
# 
# ## Show proportion of panel space taken up by each gene 
# p2 <- ggplot(data = merged_exons) + geom_line(aes(x = x_axis, y = proportion_total, group = 1), color = "Blue") + 
#   theme(axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.title.x = element_blank()) + 
#   labs(y = paste0("Proportion of", "\n", "Total (%)", "\n")) + 
#   annotate(geom = "text", x = 40, y = 5, label = paste0("Total = ", sum(merged_exons$exon_size), " nucleotides")) + 
#   geom_vline(xintercept = 9.5, linetype = 2, color = "red") 
# 
# ggarrange(p2, p1, nrow=2, ncol=1, common.legend = TRUE, legend = "right", heights = c(0.5, 2))

Prioritization of exons in the gene list of interest:

• Identify exons with 1+ cancer hotspot mutation
• Identify exons frequently mutated in cancer datasets (e.g. cBioPortal)
• Identify exons that reside in key functional protein domains (Interpro)
• Identify exons that have high GC content

Necessary files

Cancer Hotspot Datasets

## Download [3D Hotspots](http://www.3dhotspots.org/#/home) dataset and manually save as a txt file
wget http://www.3dhotspots.org/files/3d_hotspots.xls

## Download  [Cancer Hotspots](https://www.cancerhotspots.org/#/home) MAF file
cd ~/spermseq/data/hotspots
wget http://download.cbioportal.org/cancerhotspots/cancerhotspots.v2.maf.gz 
zless cancerhotspots.v2.maf.gz | grep -v "#" | grep -w "protein_coding" > cancerhotspots.v2.maf
zless cancerhotspots.v2.maf.gz | grep -v "#" | head -n 1 > cancerhotspots.v2.maf.header
cat cancerhotspots.v2.maf.header cancerhotspots.v2.maf > cancerhotspots.v2.proteincoding.maf
cat cancerhotspots.v2.proteincoding.maf | cut -f 1,2,5,6,7,38 > cancerhotspots.v2.proteincoding.columns.maf
## Copy to local directory
scp u1240855@kingspeak.chpc.utah.edu:~/spermseq/data/hotspots/cancerhotspots.v2.proteincoding.columns.maf ~/git/spermseq/twinstrand_target_gene_set/data

## Download the liftover file to convert hg19 coordinates from MAF file to hg38
wget https://hgdownload.cse.ucsc.edu/goldenpath/hg19/liftOver/hg19ToHg38.over.chain.gz 
gunzip hg19ToHg38.over.chain.gz > hg19ToHg38.over.chain
scp u1240855@kingspeak.chpc.utah.edu:~/spermseq/data/hotspots/hg19ToHg38.over.chain ~/git/spermseq/twinstrand_target_gene_set/data

Set up R environment

Identify exons with 1+ cancer 3D hotspot mutation

## Exons with 3d hotspot mutations 
if (file.exists("~/git/spermseq/script/final.Rdata") == FALSE) {
  source("~/git/spermseq/script/format_hotspot.R")
  merged_exons_3d_hotspot <- format_3D_hotspot(hotspot_file = "~/git/spermseq/twinstrand_target_gene_set/data/3d_hotspots_gao.txt",
                                               genes = genes, merged_exons = merged_exons)
}

Identify exons with 1+ cancer hotspot mutation

## Exons with snp/onp/ins/del hotspot mutations

if (file.exists("~/git/spermseq/script/final.Rdata") == FALSE) {
  source("~/git/spermseq/script/format_hotspot.R")
  hotspot_file <- "~/git/spermseq/twinstrand_target_gene_set/data/cancerhotspots.v2.proteincoding.columns.maf"
  merged_exons_hotspot <- format_hotspot(hotspot_file = hotspot_file, genes = genes, merged_exons_3d_hotspot = merged_exons_3d_hotspot)
  merged_exons_hotspot
}

Calcualte GC content of exons from merged exon list

if (file.exists("~/git/spermseq/script/final.Rdata") == FALSE) {

  ## Load in necessary packages
  library(BSgenome.Hsapiens.UCSC.hg38)
  library(BSgenome)
  library(GenomicRanges)
  
  ## Define GetGC function
  GetGC <- function(bsgenome, gr){
    
    seqs <- BSgenome::getSeq(bsgenome, gr)
    return(as.numeric(Biostrings::letterFrequency(x = seqs, letters = "GC", as.prob = TRUE)))
    
  }
  
  ## Get the IRanges
  ranges <- IRanges(start = merged_exons_hotspot$start, end = merged_exons_hotspot$stop)
  
  ## Get GRanges object
  gr <- GRanges(seqnames = paste0("chr", merged_exons_hotspot$chr), ranges=ranges)
  
  ## Get the gc content of each sequence 
  merged_exons_hotspot$gc_content <- GetGC(bsgenome = BSgenome.Hsapiens.UCSC.hg38, gr = gr)
  write.table(x = merged_exons_hotspot, file = "~/git/spermseq/twinstrand_target_gene_set/output/merged_exons_final.txt",
              sep = "\t", quote = FALSE, row.names = FALSE)
  
  ## Save the data
  save.image("~/git/spermseq/script/final.Rdata")
}

if (file.exists("~/git/spermseq/script/final.Rdata") == TRUE) {
  load("~/git/spermseq/script/final.Rdata")
}

Plots with different filters

Exons in functional domains and harbor hotspots

## Filter out exons/rows that have NA values in pfam_id column
merged_exons_pfam <- na.omit(merged_exons_hotspot)
merged_exons_pfam_hotspot <- merged_exons_pfam[hotspot_3d_exon_num > 0 | hotspot_exon_num > 0]

## Calculate proportion of panel space taken up by each gene 
total <- sum(merged_exons_pfam_hotspot$exon_size)
merged_exons_pfam_hotspot <- rbindlist(lapply(unique(merged_exons_pfam_hotspot$gene_id), function(x, merged_exons_pfam_hotspot) {
  temp <- merged_exons_pfam_hotspot[gene_id == x]
  temp$total_nucleotides <- sum(temp$exon_size)
  temp$total_exons <- nrow(temp)
  temp$transcript_num <- (temp$transcript_id %>% strsplit(., split = " | "))[[1]] %>% unique() %>% length()
  temp$avg_exon_size <- round(mean(temp$exon_size), digits = 2)
  return(temp)
}, merged_exons_pfam_hotspot=merged_exons_pfam_hotspot))
merged_exons_pfam_hotspot$proportion_total <- merged_exons_pfam_hotspot$total_nucleotides/total*100

source("~/git/spermseq/script/plot_df.R")
p <- plot_df(df = merged_exons_pfam_hotspot, genes = genes)
p

Exons in functional domains

## Filter out exons/rows that have NA values in pfam_id column
merged_exons_pfam <- na.omit(merged_exons_hotspot)

## Calculate proportion of panel space taken up by each gene 
total <- sum(merged_exons_pfam$exon_size)
merged_exons_pfam <- rbindlist(lapply(unique(merged_exons_pfam$gene_id), function(x, merged_exons_pfam) {
  temp <- merged_exons_pfam[gene_id == x]
  temp$total_nucleotides <- sum(temp$exon_size)
  temp$total_exons <- nrow(temp)
  temp$transcript_num <- (temp$transcript_id %>% strsplit(., split = " | "))[[1]] %>% unique() %>% length()
  temp$avg_exon_size <- round(mean(temp$exon_size), digits = 2)
  return(temp)
}, merged_exons_pfam=merged_exons_pfam))
merged_exons_pfam$proportion_total <- merged_exons_pfam$total_nucleotides/total*100

source("~/git/spermseq/script/plot_df.R")
p <- plot_df(df = merged_exons_pfam, genes = genes)
p

Transcript Number Exon Filter for Pfam Exons: 20

## Subset the final dataset
final <- merged_exons_pfam[transcript_proportion >= 0.2]

## Generate plot 
source("~/git/spermseq/script/plot_df.R")
p <- plot_df(df = final, genes = genes)
p

Transcript Number Exon Filter for Pfam Exons: 40

## Subset the final dataset
final <- merged_exons_pfam[transcript_proportion >= 0.4]

## Generate plot 
source("~/git/spermseq/script/plot_df.R")
p <- plot_df(df = final, genes = genes)
p

Transcript Number Filter for Pfam Exons: 50

## Subset the final dataset
final <- merged_exons_pfam[transcript_proportion >= 0.5]

## Generate plot 
source("~/git/spermseq/script/plot_df.R")
p <- plot_df(df = final, genes = genes)
p

Transcript Number Filter for Pfam Exons: 60

## Subset the final dataset
final <- merged_exons_pfam[transcript_proportion >= 0.6]

## Generate plot 
source("~/git/spermseq/script/plot_df.R")
p <- plot_df(df = final, genes = genes)
p

Transcript Number Exon Filter for Pfam Exons: 80

## Subset the final dataset
final <- merged_exons_pfam[transcript_proportion >= 0.8]

## Generate plot 
source("~/git/spermseq/script/plot_df.R")
p <- plot_df(df = final, genes = genes)
p

Transcript Number Exon Filter for Pfam Exons: 100

## Subset the final dataset
final <- merged_exons_pfam[transcript_proportion == 1]

## Generate plot 
source("~/git/spermseq/script/plot_df.R")
p <- plot_df(df = final, genes = genes)
p

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